A Path Optimization Strategy for USV-UAV Collaborative Exploration of
Maritime Target with Energy Constraints
Abstract
For this study, we focus on the exploration of maritime areas that
contain accident-prone points, such as illegal riding, unauthorized
boarding, illegal fishing, and smuggling. This exploration is carried
out using a cooperative system consisting of an Unmanned Aerial Vehicle
(UAV) and an Unmanned Surface Vehicle (USV). The goal is to allow the
USV-UAV system to efficiently explore all of the accident-prone points
while minimizing the UAV’s energy usage. Specifically, we aim to achieve
this objective while keeping travel time as short as possible. The
collaborative exploration system leverages the strengths of both the UAV
and the USV. The UAV is deployed to explore hazardous areas that are
inaccessible by the USV, while the USV doubles as a mobile charging
station, resolving the UAV’s energy limitation issue. The proposed
algorithm for this subject paper, called the Collaborative Accident
Searching Routing Optimization (CASRO) algorithm, utilizes the benefits
of both the Lazy Theta* algorithm and the Improved Ant Colony algorithm
to optimize the path of a cooperative system between USV and UAV. With
CASRO, we aim to address the two key limitations of the USV, namely poor
flexibility, and the UAV’s limited energy simultaneously. Finally, the
effectiveness and superiority of the proposed planning strategy in
target exploration is verified by numerical simulations of randomly
distributed maritime areas with accident-prone points.